In modern days, weather forecasting is widely used in a variety of situations. Weather forecasting can be divided, for example, into the following types depending on the purpose. Agricultural forecasts include detailed characteristics of the atmospheric precipitation; sea and river forecasts include detailed descriptions of wind, waves, atmospheric phenomena, air temperature; aviation forecasts include detailed descriptions of wind, visibility, atmospheric phenomena, cloudiness, air temperature, and forecasts for general consumer use include brief information about cloud cover, precipitation, atmospheric phenomena, wind, temperature, humidity, atmospheric pressure, etc.
A user can receive current weather parameters data for a given territory from a variety of sources, e.g. radio stations, television, the Internet, etc. At the same time, the accuracy of the current weather parameters can be considered to be reliable enough as it is based on the real data received from the weather stations. FIG. 1 shows a screenshot 100 of the Realmeteo web-site, which can be accessed by the user via the Internet using known means. The screenshot 100 shows current time and date 102, the territory 104 (Moscow), current weather parameters 106 and data 108 in respect to the location of a weather station, from which the current weather parameters 106 were received. The accuracy of the current weather parameters depends on the accuracy of equipment of the data provider, in this example, the weather station located in the Vnukovo airport area. At present, the world meteorological organization (WMO) collects meteorological, climatological, hydrological as well as marine and oceanographic data around the world via more than 15 satellites, 100 anchored buoys, 600 drifting buoys, 3,000 aircrafts, 7,300 ships and around 10,000 ground stations. Member countries of the WMO have access to this data.
The user can also get data representative of a forecasted weather parameters for a given moment in time in the future (the given moment in the future, after the current moment of time) for a given territory from either the same and/or other sources. However, the accuracy of the forecast parameters depends on the time of the forecast (i.e. how far in advance the forecast is being executed), the forecast method, the given territory and many other criteria. For example, depending on the time of the forecast vis-a-vis the time in the future for which the forecast is being generated, the forecast can be categorized as: a very-short-range forecast—up to 12 hours; a short-range forecast—from 12 to 36 hours; a mid-range forecast—from 36 hours to 10 days; a long-range forecast—from 10 days to a season (3 months); and a very-long-range forecast—more than 3 months (a year, a few years). The accuracy of the forecasts generally decreases the the increase of time between the time when the forecast is generated and the time for which the forecast is generated. As an example, the accuracy (reliability) of the very-short-range forecast can be 95-96%, the short-range forecast—85-95%, the mid-range forecast—65-80%, the long-range forecast—60-65%, and the very-long-range forecast—not more than 50%.
The accuracy also depends on the forecasting methods used for generating the forecast. At the moment, the numerical models of weather forecasting are generally considered to be the most accurate and the most reliable of all the known forecasting methods. Put another way, forecasting methods implemented by computational systems for weather forecasting using current weather parameters data. These computational systems can used raw data provided by weather balloons, weather satellites, and ground weather stations.
The accuracy of the weather forecasts is a general concern in the industry, and several known technologies have attempted to address this concern.
U.S. Pat. No. 6,778,929 discloses method and system for estimating (forecasting) meteorological quantities. Estimation method for obtaining estimation results of meteorological quantities in a specified area during a specified future period, including steps of: provisionally creating a meteorological time-series model from historical data of the meteorological quantities observed in the specified area; adjusting parameters of the created time-series model on the basis of long-range weather forecast data for wider area, which contains future meteorological tendency relative to normal years, to adjust the created time-series model; and conducting simulation using the adjusted time-series model to obtain the estimation results.
US patent application 2010/027,4542 discloses a method and software program for providing a weather prediction of atmospheric parameters for an aircraft, includes collecting at least one of a statistical description of the weather forecast or the historical weather data, processing current atmospheric data received from sensors on-board the aircraft, forming modeled data based on the processed current atmospheric data and the at least one of a statistical description of the weather forecast or the historical weather data, blending the modeled data with the at least one of a statistical description of the weather forecast or the historical weather data, and predicting atmospheric parameters based on the blending step.
U.S. Pat. No. 5,461,699 discloses a a system and method for forecasting that combines a neural network with a statistical forecast is presented. A neural network having an input layer, a hidden layer, and an output layer with each layer having one or more nodes is presented. Each node in the input layer is connected to each node in the hidden layer and each node in the hidden layer is connected to each node in the output layer. Each connection between nodes has an associated weight. One node in the input layer is connected to a statistical forecast that is produced by a statistical model. All other nodes in the input layer are connected to a different historical datum from the set of historical data. The neural network being operative by outputting a forecast, the output of the output layer nodes, when presented with input data. The weights associated with the connections of the neural network are first adjusted by a training device. The training device applies a plurality of training sets to the neural network, each training set consisting of historical data, an associated statistical output and a desired forecast, with each set of training data the training device determines a difference between the forecast produced by the neural network given the training data and the desired forecast, the training device then adjusts the weights of the neural network based on the difference.
The present technology arises from an observation made by the inventor(s) that there is at least one technical problem, known in the art, related to the need to improve the accuracy of weather forecasts.